29 citations · 36 across the 3 of their papers we have counts for
5 papers · 1 filter
Online Learned Continual Compression with Adaptive Quantization Modules
Lucas Caccia, Eugene Belilovsky, Massimo Caccia +1
We introduce and study the problem of Online Continual Compression, where one attempts to simultaneously learn to compress and store a representative dataset from a non i.i.d data…
Online Continual Learning with Maximally Interfered Retrieval
Rahaf Aljundi, Lucas Caccia, Eugene Belilovsky +4
Continual learning, the setting where a learning agent is faced with a never ending stream of data, continues to be a great challenge for modern machine learning systems. In partic…
Decoupled Greedy Learning of CNNs
Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon
A commonly cited inefficiency of neural network training by back-propagation is the update locking problem: each layer must wait for the signal to propagate through the full networ…
Greedy Layerwise Learning Can Scale to ImageNet
Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon
Shallow supervised 1-hidden layer neural networks have a number of favorable properties that make them easier to interpret, analyze, and optimize than their deep counterparts, but…
Scattering Networks for Hybrid Representation Learning
Edouard Oyallon, Sergey Zagoruyko, Gabriel Huang +4
Scattering networks are a class of designed Convolutional Neural Networks (CNNs) with fixed weights. We argue they can serve as generic representations for modelling images. In par…